Automated Detection Method for Clustered Microcalcification in Mammogram Image Based on Statistical Textural Features

Abstract

Abstract-Breast cancer is the most frightening cancer for women in the world. The current problem that closely related with this issue is how to deal with small calcification part inside the breast called micro calcification (MC). As a preventive way, a breast screening examination called mammogram is provided. Mammogram image with a considerable amount of MC has been a problem for the doctor and radiologist when they should determine correctly the region of interest, in this study is clustered MC. Therefore, we propose to develop an automated method to detect clustered MC utilizing two main methods, multi-branch standard deviation analysis for clustered MC detection and surrounding region dependence method for individual MC detection. Our proposed method was resulting in 70.8% of classification rate, then for the sensitivity and specificity obtained 79% and 87%, respectively. The gained results are adequately promising to be more developed in some areas.

Method for Disaster Area Detection with Just One SAR Data Acquired on the Day After Earthquake Based on YOLOv8

“…Convolutional neural network considering physical processes and its application to disaster detection is proposed [15]. More recently, a method for frequent high resolution of optical sensor image acquisition using satellite-based SAR image based on GAN (Generative Adversarial Network) for disaster mitigation is proposed [16].…”

“…Learn with YOLOv8 14 and Detectron2 15 using the disaster area obtained from the Geospatial Information Authority of Japan's aerial photo interpretation, Planet Dove 16 , and Pleiades Neo 17 for annotation. Roboflow 18 is used for annotation and augmentation as well as YOLOv8 and Detectron2 is used for creation of learned model for disaster areas.…”